http://iet.metastore.ingenta.com
1887

Use of hyperspectral imaging for cake moisture and hardness prediction

Use of hyperspectral imaging for cake moisture and hardness prediction

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Industrial baking of sponge cakes requires various quality indicators to be measured during production such as moisture content and sponge hardness. Existing techniques for measuring these properties require randomly selected sponges to be removed from the production line before samples are manually cut out of each sponge in a destructive way for testing. These samples are subsequently processed manually using dedicated analysers to measure moisture and texture properties in a lengthy process, which can take a skilled operator around 20 min to complete per sponge. In this study, the authors present a new, single sensor hyperspectral imaging approach, which has the potential to measure both sponge moisture content and hardness simultaneously. In the last decade, hyperspectral imaging systems have reduced in cost and size and, as a result, they are becoming widely used in a number of industries and research areas. Recently, there has been an increased use of this technology in the food industry and in food science applications and research. The application of this technology in the cake production environment, empowered by sophisticated signal and image processing techniques and prediction algorithms as presented in this study has the potential to provide on-line, real-time, stand-off cake quality monitoring.

References

    1. 1)
      • 1. Goetz, A.F.H., Vane, G., Solomon, J.E., et al: ‘Imaging spectrometry for earth remote sensing’, Science, 1985, 228, pp. 11471153.
    2. 2)
      • 2. Hsuan, R., Chein, I.C.: ‘Automatic spectral target recognition in hyperspectral imagery’, IEEE Trans. Aerosp. Electron. Syst., 2003, 39, pp. 12321249.
    3. 3)
      • 3. Bannon, D.: ‘Hyperspectral imaging: cubes and slices’, Nat. Photonics, 2009, 3, pp. 627629.
    4. 4)
      • 4. Kurz, T., Buckley, S., Howell, J.: ‘Close-range hyperspectral imaging for geological field studies: workflow and methods’, Int. J. Remote Sens., 2013, 34, (5), pp. 17981822.
    5. 5)
      • 5. Denk, M., Gläßer, C., Kurz, T.H., et al: ‘Mapping of iron and steelwork by-products using close range hyperspectral imaging: a case study in Thuringia, Germany’, Eur. J. Sens., 2015, 48, (1), pp. 489509.
    6. 6)
      • 6. Mirschel, G., Daikos, O., Steckert, C., et al: ‘Characterisation of sizeson textiles by in-line NIR chamical imaging’. OCM 2017 – Optical Characterization of Materials, Karlsruhe, 2017.
    7. 7)
      • 7. Mirschel, G., Daikos, O., Scherzer, T., et al: ‘Near-infrared hyperspectral imaging of lamination and finishing processes in textile technology’, NIR News, 2017, 28, (1), pp. 2025.
    8. 8)
      • 8. Blanch-Perez-del-Notario, C., Lambrechts, A.: ‘New paper on hyperspectral imaging for textile recycling’. 1 January 2016. Available at http://www.resyntex.eu/images/downloads/HSI_2016_paper_final.pdf, accessed 11 May 2017.
    9. 9)
      • 9. Roggo, Y., Edmond, A., Chalus, P., et al: ‘Infrared hyperspectral imaging for qualitative analysis of pharmaceutical solid forms’, Anal. Chim. Acta, 2005, 535, pp. 7987.
    10. 10)
      • 10. Mirschel, G., Daikos, O., Scherzer, T., et al: ‘Hyperspectral imaging used for in-line monitoring in textile technology’. IASIM – 6th Conf. in Spectral Imaging, Chamonix-Mont-Blanc, France, 2016.
    11. 11)
      • 11. Marshall, S., Kelman, T., Qiao, T., et al: ‘Hyperspectral imaging for food applications’. 23rd European Signal Processing Conf. (EUSIPCO), Nice, 2015.
    12. 12)
      • 12. Wu, D., Sun, D.-W.: ‘Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: a review – part II: applications’, Innovative Food Sci. Emerg. Technol., 2013, 19, pp. 1528.
    13. 13)
      • 13. Dale, M.: ‘Trash talk’ (Imaging & Machine Vision, Europe, 2016), pp. 2022.
    14. 14)
      • 14. Lu, Y., Huang, Y., Lu, R.: ‘Innovative hyperspectral imaging-based techniques for quality evaluation of fruits and vegetables: a review’, Appl. Sci., 2017, 7, (189), pp. 1421.
    15. 15)
      • 15. Jayas, D.S., Singh, C.B., Paliwal, J.: ‘Classification of wheat kernels using near-infrared reflectance hyperspectral imaging’, in Da-Wen, Sun (Eds.): ‘Hyperspectral imaging for food quality analysis and control’, (Academic Press/Elsevier, San Diego, California, USA, 2010, 1st edn.) pp. 449470.
    16. 16)
      • 16. Huang, H., Liu, L., Ngadi, M.O.: ‘Recent developments in hyperspectral imaging for assessment of food quality and safety’, Sensors, 2014, 14, pp. 72487276.
    17. 17)
      • 17. Naganathan, G.K., Grimes, L.M., Subbiah, J., et al: ‘Visible/near-infrared hyperspectral imaging for beef tenderness prediction’, Comput. Electron. Agric., 2008, 64, (2), pp. 225233.
    18. 18)
      • 18. Qiao, T., Ren, J., Craigie, C., et al: ‘Comparison between near infrared spectroscopy and hyperspectral imaging in predicting beef eating quality’. Hyperspectral Imaging and Applications Conf. (HSI 2014), Coventry, 2014.
    19. 19)
      • 19. Qiao, T., Ren, J., Craigie, C., et al: ‘Quantitative prediction of beef quality using visible and NIR spectroscopy with large data samples under industry conditions’, J. Appl. Spectrosc., 2015, 82, (1), pp. 18.
    20. 20)
      • 20. Qiao, T., Ren, J., Zabalza, J., et al: ‘Prediction of lamb eating quality using hyperspectral imaging’. OCM (Optical Characterization of Materials) 2015, Karlsruhe, 2015.
    21. 21)
      • 21. Andresen, M.S., Dissing, B.S., Løje, H.: ‘Quality assessment of butter cookies applying multispectral imaging’, Food. Sci. Nutr., 2013, 1, (4), p. pp. 315323.
    22. 22)
      • 22. Bensaeed, O., Shariff, A., Mahmud, A.B., et al: ‘Oil palm fruit grading using a hyperspectral device and machine learning algorithm’. 7th IGRSM Int. Remote Sensing & GIS Conf. and Exhibition, Kuala Lumpur, 2014.
    23. 23)
      • 23. Li, J., Rao, X., Ying, Y.: ‘Detection of common defects on oranges using hyperspectral reflectance imaging’, Comput. Electron. Agric., 2011, 78, pp. 3848.
    24. 24)
      • 24. Lu, R.: ‘Detection of bruise on apples using near-infrared hyperspectral imaging’, Trans. ASAE, 2003, 46, (2), pp. 18.
    25. 25)
      • 25. Shuqin, Y., Dongjian, H., Jifeng, N.: ‘Predicting wheat kernels’ protein content by near infrared hyperspectral imaging’, Int. J. Agric. Biol. Eng., 2016, 9, (2), pp. 163170.
    26. 26)
      • 26. Talensa, P., Morab, L., Morsyc, N., et al: ‘Prediction of water and protein contents and quality classification of spanish cooked ham using NIR hyperspectral imaging’, J. Food Eng., 2013, 117, (3), pp. 272280.
    27. 27)
      • 27. Apan, A., Kelly, R., Phinn, S., et al: ‘Predicting grain protein content in wheat using hyperspectral sensing of in-season crop canopies and partial least squares regression’, Int. J. Geoinf., 2006, 2, pp. 93108.
    28. 28)
      • 28. Manley, M.: ‘Near-infrared spectroscopy and hyperspectral imaging: non-destructive analysis of biological materials’, Chem. Soc. Rev., 2014, 43, pp. 82008214.
    29. 29)
      • 29. Kobori, H., Gorretta, N., Rabatel, G., et al: ‘Applicability of Vis-NIR hyperspectral imaging for monitoring wood moisture content (MC)’, Holzforschung, 2013, 67, (3), pp. 307314.
    30. 30)
      • 30. Tsuchikawa, S., Kobori, H.: ‘A review of recent application of near infrared spectroscopy to wood science and technology’, J. Wood Sci., 2015, 61, (3), p. pp. 213220.
    31. 31)
      • 31. Leblon, B.: ‘Overview on sensing technologies for real-time monitoring of wood properties’. IASIM – 6th Conf. in Spectral Imaging, Chamonix-Mont-Blanc, France, 2016.
    32. 32)
      • 32. Pu, Y.-Y., Sun, D.-W.: ‘Hyperspectral imaging in visualizing the non-uniform drying of mango slices during hot-air and microwave-vacuum drying’. IASIM – 6th Conf. in Spectral Imaging, Chamonix-Mont-Blanc, France, 2016.
    33. 33)
      • 33. Liu, Z., Møller, F.: ‘Bread water content measurement based on hyperspectral imaging’. Scandinavian Workshop on Imaging Food Quality 2011, Ystad, 2011.
    34. 34)
      • 34. Whitworth, M.B., Millar, S.J., Chau, A.: ‘Food quality assessment by NIR hyperspectral imaging’. Proc. SPIE 7676, Sensing for Agriculture and Food Quality and Safety II, Orlando, USA, 2010.
    35. 35)
      • 35. Bri, C.: ‘Bakery product development’. 2013. Available at http://www.campdenbri.co.uk/services/bakery-product-development.php.
    36. 36)
      • 36. Davidson, M.W.: ‘ZEISS microscopy online campus|tungsten-halogen lamps’. Available at: http://zeiss-campus.magnet.fsu.edu/articles/lightsources/tungstenhalogen.html, accessed 15 November 2018.
    37. 37)
      • 37. Labsphere Inc.: ‘Spectralon diffuse reflectance standards’. Available at https://www.labsphere.com/site/assets/files/1827/pb-13021-000_rev_02_og_spectralon.pdf, accessed 15 November 2018.
    38. 38)
      • 38. BROOKFIELD ENGINEERING LABORATORIES, INC: ‘BROOKFIELD CT3 texture analyzer, operating instructions, manual No. M08-372-E0315’. Available at http://www.brookfieldengineering.com/download/files/CT3manual.pdf, accessed 11 May 2017.
    39. 39)
      • 39. Kamruzzaman, M., Makino, Y., Oshita, S.: ‘Online monitoring of red meat color using hyperspectral imaging’, Meat Sci., 2016, 116, pp. 110117.
    40. 40)
      • 40. Rodgers, J.L., Nicewander, W.A.: ‘Thirteen ways to look at the correlation coefficient’, Am. Stat., 1988, 42, (1), pp. 5966.
    41. 41)
      • 41. University of the West of England: ‘Pearson's correlation coefficient’. Available at http://learntech.uwe.ac.uk/da/Default.aspx?pageid=1442, accessed 11 May 2017.
    42. 42)
      • 42. Geladi, P., Kowalski, B.R.: ‘Partial least squares regression: a tutorial’, Anal. Chim. Acta, 1986, 185, pp. 117.
    43. 43)
      • 43. Wold, S., Sjöström, M., Eriksson, L.: ‘PLS regression: a basic tool of chemometrics’, Chemometr. Intell. Lab. Syst., 2001, 58, pp. 109130.
    44. 44)
      • 44. Wold, S., Eriksson, L., Trygg, J., et al: ‘The PLS method – partial least squares projections to latent structures – and its applications in industrial RDP (research, development, and production)’ (Umea University, Umea, Sweden, 2004).
    45. 45)
      • 45. Abdi, H.: ‘Partial least square regression, projection on latent structures regression, PLS-regression’, Wiley Interdiscip. Rev.: Comput. Stat., 2010, 2, pp. 97106.
    46. 46)
      • 46. Li, H., Lavin, M.A., LeMaster, R.J.: ‘Fast hough transform: a hierarchical approach’, Comput. Vision Graph. Image Process., 1986, 36, pp. 139161.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2018.5106
Loading

Related content

content/journals/10.1049/iet-ipr.2018.5106
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address